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纳米红外光谱 (nano-infrared spectroscopy, nano-IR) 技术能够突破光的衍射极限, 实现约10 nm空间分辨率的红外光谱检测, 是研究纳米尺度物质化学成分和结构的重要技术手段. 然而, 由于纳米物质的尺寸与红外光的波长存在较大失配, 导致其红外吸收信号微弱. 本文理论提出了一种基于纳米腔室的声学型石墨烯等离激元 (nanocavity-acoustic graphene plasmon, n-AGP) 可调谐增强nano-IR检测平台. 该平台可实现超高光场压缩 (模式体积Vn-AGP
$ \approx $ 10–7$ {\lambda }_{0}^{3} $ , λ0 = 6.25 μm)和约50倍电场增强的n-AGP激发. 通过调控金纳米腔室结构和石墨烯费米能级, 我们实现了n-AGP的宽频段动态调控(1290—2124 cm–1). 此外, 由于n-AGP的电磁场高度局域在纳米腔室内, 具有高的探测灵敏度, 可实现单个蛋白质颗粒酰胺I带和酰胺II带振动指纹特征的探测 (灵敏度提高约9倍). 这一基于n-AGP的增强结构拓展了nano-IR技术在单分子尺度的表征能力, 可广泛应用于生物、催化等领域.Nano-infrared spectroscopy (nano-IR) technology can exceed the diffraction limit of light, achieving infrared spectroscopic detection with a spatial resolution of about 10 nm, which is an important technical means for studying the chemical composition and structure of molecules on a nanoscale. However, the weak infrared absorption signals of nanoscale materials pose a significant challenge due to the large mismatch between their dimensions and the wavelength of infrared light. The infrared absorption signals of molecular vibrational modes are proportional to the squares of the electromagnetic field intensities at their positions, implying that higher electromagnetic field intensity can significantly improve the sensitivity of molecular detection. Acoustic graphene plasmons (AGPs), excited by the interaction between free charges in graphene and image charges in metal, exhibit strong optical field localization and electromagnetic field enhancement. These properties make AGPs an effective platform for enhancing nano-IR detection sensitivity. However, the fabrication of graphene nanostructures often introduces numerous edge defects due to the limitations of nanofabrication techniques, significantly reducing the electromagnetic field enhancement observed in experiments. Here, we use finite element simulation to theoretically propose a tunable enhanced nano-IR detection platform based on nanocavity-acoustic graphene plasmons (n-AGPs), which utilizes a graphene/air gap/gold nanocavity structure. This platform avoids needing the nanofabrication of graphene, thereby preventing defects and contamination from being introduced in processes such as electron beam exposure and plasma etching. By plotting the dispersion of n-AGP, it is found that n-AGP has a high wavelength compression capability comparable to AGP (λ0/λAGP = 48). Additionally, due to the introduction of the gold nanocavity structure, n-AGP possess an extremely small mode volume (Vn-AGP ≈ 10–7$ {{ \lambda }}_{0}^{3} $ , λ0 = 6.25 μm). By calculating the electric field intensity distribution (|Enorm|) and the normalized electric field intensity spectrum (i.e. the relationship between frequency and |Ez|/|Ez0|) of the n-AGP structure, it is evident that due to the high electron density on the gold surface, electromagnetic waves can be reflected from the boundaries of the gold nanocavity and resonantly enhanced within the nanocavity. At the resonant frequency of n-AGP (1800 cm–1), the electric field inside the cavity is enhanced by about 50 times. In contrast, at similar resonant frequencies, the electric field enhancement factor of Graphene plasmon (resonant frequency 1770 cm–1) and AGP (resonant frequency 1843 cm–1) are approximately 3 and 2 times, respectively, significantly lower than that of n-AGP. Furthermore, by placing a protein film (60 nm wide and 10 nm high) under the graphene, we calculate the spectral dip depths caused by Fano resonance between n-AGP and AGP with the vibrational modes of protein molecules, thereby validating the enhancement factors of different modes for protein vibrational mode infrared absorption. For the amide-I band of proteins, the detection sensitivity of n-AGP is about 60 times higher than that of AGP. Additionally, we find that by adjusting the structural parameters of the gold nanocavity, including cavity depth, width, and surface roughness, the response frequency band of n-AGP can be modulated (from 1290 to 2124 cm–1). Specifically, as the cavity depth increases, the electric field enhancement of n-AGP is improved, and the wavelength compression capability of n-AGP decreases, causing the resonant frequency to be blue-shifted (from 1793 to 2124 cm–1). As the cavity width increases, the resonant frequency of n-AGP is red-shifted (from 1793 to 1290 cm–1), and the effectiveness of the gold nanocavity boundary in reflecting the resonant electric field within the cavity diminishes, resulting in a decrease in the electric field enhancement factor. With the gradual increase in the roughness of the gold nanocavity bottom, the effective depth of the gold nanocavity increases, causing the n-AGP resonant frequency to be blue-shifted (from 1793 to 1861 cm–1) and the electric field enhancement factor to increase. Moreover, by adjusting the Fermi level of graphene (from 0.3 to 0.6 eV), we achieve dynamic tuning of n-AGP (from 1355 to 1973 cm–1). As the Fermi level of graphene increases, the wavelength compression capability of n-AGP decreases, resulting in a blue-shift in the resonant frequency. Finally, by optimizing the structural parameters and Fermi level of n-AGP, and placing protein particles of different sizes (20, 15, and 10 nm high, all 10 nm wide) into the graphene/gold nanocavity structure, we verify the protein detection capability of n-AGP-enhanced nano-IR. We find that n-AGP can detect the vibrational fingerprint features of the amide-I band and amide-II band. For protein films (60 nm wide and 10 nm high), the sensitivity increased by approximately 300 times, and for a single protein particle (10 nm wide and 10 nm high), the sensitivity increased by approximately 9 times. This enhanced structure based on n-AGP holds promise for providing an important detection platform for nanoscale material characterization and single-molecule detection, with broad application potential in biomedicine, materials science, and geology.
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Keywords:
- graphene /
- nanocavity /
- plasmon /
- surface enhanced infrared spectroscopy
[1] Yao Z, Xu S, Hu D, Chen X, Dai Q, Liu M 2020 Adv. Opt. Mater. 8 1901042
Google Scholar
[2] Chen X, Hu D, Mescall R, You G, Basov D N, Dai Q, Liu M 2019 Adv. Mater. 31 1804774
Google Scholar
[3] Lahiri B, Holland G, Centrone A 2013 Small 9 439
Google Scholar
[4] Centrone A 2015 Annu. Rev. Anal. Chem. 8 101
Google Scholar
[5] Katzenmeyer A M, Holland G, Kjoller K, Centrone A 2015 Anal. Chem. 87 3154
Google Scholar
[6] Dazzi A, Glotin F, Carminati R 2010 J. Appl. Phys. 107 124519
Google Scholar
[7] Schwartz J J, Jakob D S, Centrone A 2022 Chem. Soc. Rev. 51 5248
Google Scholar
[8] Wang L, Wang H, Xu X G 2022 Chem. Soc. Rev. 51 5268
Google Scholar
[9] Wang L, Wang H, Wagner M, Yan Y, Jakob D S, Xu X G 2017 Sci. Adv. 3 e1700255
Google Scholar
[10] Wang H, Gonzalez-Fialkowski J M, Li W, Xie Q, Yu Y, Xu X G 2021 Anal. Chem. 93 3567
Google Scholar
[11] Davies-Jones J A, Davies P R 2022 Mater. Chem. Front. 6 1552
Google Scholar
[12] Rajapaksa I, Uenal K, Wickramasinghe H K 2010 Appl. Phys. Lett. 97 073121
Google Scholar
[13] Jahng J, Fishman D A, Park S, Nowak D B, Morrison W A, Wickramasinghe H K, Potma E O 2015 Acc. Chem. Res. 48 2671
Google Scholar
[14] Xue M, Ye S, Ma X, Ye F, Wang C, Zhu L, Yang Y, Chen J 2022 J. Am. Chem. Soc. 144 20278
Google Scholar
[15] Gamage S, Howard M, Makita H, Cross B, Hastings G, Luo M, Abate Y 2018 PLoS One 13 e0199112
Google Scholar
[16] Kim S Y, Khanal D, Kalionis B, Chrzanowski W 2019 Nat. Protoc. 14 576
Google Scholar
[17] Goikoetxea M, Amenabar I, Chimenti S, Paulis M, Leiza J R, Hillenbrand R 2021 Macromolecules 54 995
Google Scholar
[18] Tri P N, Prud’homme R E 2018 Macromolecules 51 181
Google Scholar
[19] Morsch S, Liu Y, Lyon S B, Gibbon S R 2016 ACS Appl. Mater. Interfaces 8 959
Google Scholar
[20] Yang J, Hatcherian J, Hackley P C, Pomerantz A E 2017 Nat. Commun. 8 2179
Google Scholar
[21] Hassenkam T, Andersson M P, Dalby K N, Mackenzie D M A, Rosing M T 2017 Nature 548 78
Google Scholar
[22] Kebukawa Y, Kobayashi H, Urayama N, Baden N, Kondo M, Zolensky M E, Kobayashi K 2019 Proc. Natl. Acad. Sci. U. S. A. 116 753
Google Scholar
[23] Nishida J, Otomo A, Koitaya T, Shiotari A, Minato T, Iino R, Kumagai T 2024 Nano Lett. 24 836
Google Scholar
[24] Tang F, Bao P, Su Z 2016 Anal. Chem. 88 4926
Google Scholar
[25] Patabendigedara S, Nowak D, Nancarrow M J B, Clark S M 2021 Rev. Sci. Instrum. 92 023103
Google Scholar
[26] Yang X, Sun Z, Low T, Hu H, Guo X, Garcia de Abajo F J, Avouris P, Dai Q 2018 Adv. Mater. 30 e1704896
Google Scholar
[27] Hu H, Yang X, Zhai F, Hu D, Liu R, Liu K, Sun Z, Dai Q 2016 Nat. Commun. 7 12334
Google Scholar
[28] Wu C, Guo X, Duan Y, Lü W, Hu H, Hu D, Chen K, Sun Z, Gao T, Yang X, Dai Q 2022 Adv. Mater. 34 2110525
Google Scholar
[29] Rodrigo D, Limaj O, Janner D, Etezadi D, García de Abajo F J, Pruneri V, Altug H 2015 Science 349 165
Google Scholar
[30] Hu H, Yang X, Guo X, Khaliji K, Biswas S R, García de Abajo F J, Low T, Sun Z, Dai Q 2019 Nat. Commun. 10 1131
Google Scholar
[31] Bareza N, Jr, Gopalan K K, Alani R, Paulillo B, Pruneri V 2020 ACS Photonics 7 879
Google Scholar
[32] Alonso-González P, Nikitin A Y, Gao Y, Woessner A, Lundeberg M B, Principi A, Forcellini N, Yan W, Vélez S, Huber A J, Watanabe K, Taniguchi T, Casanova F, Hueso L E, Polini M, Hone J, Koppens F H L, Hillenbrand R 2017 Nat. Nanotechnol. 12 31
Google Scholar
[33] Menabde S G, Lee I H, Lee S, Ha H, Heiden J T, Yoo D, Kim T T, Low T, Lee Y H, Oh S H, Jang M S 2021 Nat. Commun. 12 938
Google Scholar
[34] Epstein I, Alcaraz D, Huang Z, Pusapati V V, Hugonin J P, Kumar A, Deputy X M, Khodkov T, Rappoport T G, Hong J Y, Peres N M R, Kong J, Smith D R, Koppens F H L 2020 Science 368 1219
Google Scholar
[35] Lundeberg M B, Gao Y, Asgari R, Tan C, Van Duppen B, Autore M, Alonso-González P, Woessner A, Watanabe K, Taniguchi T, Hillenbrand R, Hone J, Polini M, Koppens F H L 2017 Science 357 187
Google Scholar
[36] Alcaraz Iranzo D, Nanot S, Dias E J C, Epstein I, Peng C, Efetov D K, Lundeberg M B, Parret R, Osmond J, Hong J Y, Kong J, Englund D R, Peres N M R, Koppens F H L 2018 Science 360 291
Google Scholar
[37] Chen S, Autore M, Li J, Li P, Alonso-Gonzalez P, Yang Z, Martin-Moreno L, Hillenbrand R, Nikitin A Y 2017 ACS Photonics 4 3089
Google Scholar
[38] Olmon R L, Raschke M B 2012 Nanotechnology 23 444001
Google Scholar
[39] Low T, Avouris P 2014 ACS Nano 8 1086
Google Scholar
[40] Adato R, Altug H 2013 Nat. Commun. 4 2154
Google Scholar
[41] Du X, Skachko I, Barker A, Andrei E Y 2008 Nat. Nanotechnol. 3 491
Google Scholar
[42] Rickhaus P, Maurand R, Liu M H, Weiss M, Richter K, Schönenberger C 2013 Nat. Commun. 4 2342
Google Scholar
[43] Dorgan V E, Behnam A, Conley H J, Bolotin K I, Pop E 2013 Nano Lett. 13 4581
Google Scholar
[44] Hu H, Yu R, Teng H, Hu D, Chen N, Qu Y, Yang X, Chen X, McLeod A S, Alonso-González P, Guo X, Li C, Yao Z, Li Z, Chen J, Sun Z, Liu M, García de Abajo F J, Dai Q 2022 Nat. Commun. 13 1465
Google Scholar
[45] Lu Y H, Morales C, Zhao X, van Spronsen M A, Baskin A, Prendergast D, Yang P, Bechtel H A, Barnard E S, Ogletree D F, Altoe V, Soriano L, Schwartzberg A M, Salmeron M 2020 Nano Lett. 20 6364
Google Scholar
[46] Farmer D B, Rodrigo D, Low T, Avouris P 2015 Nano Lett. 15 2582
Google Scholar
[47] Zhuang B, Li S, Li S, Yin J 2021 Carbon 173 609
Google Scholar
[48] Dregely D, Neubrech F, Duan H, Vogelgesang R, Giessen H 2013 Nat. Commun. 4 2237
Google Scholar
[49] Kawata S 2001 Near-field Optics and Surface Plasmon Polaritons (Vol. 81) (Berlin: Springer Science & Business Media) p163
[50] Miao X, Luk T S, Liu P Q 2022 Adv. Mater. 34 2107950
Google Scholar
[51] Guo Q, Li C, Deng B, Yuan S, Guinea F, Xia F 2017 ACS Photonics 4 2989
Google Scholar
[52] Schwaighofer A, Montemurro M, Freitag S, Kristament C, Culzoni M J, Lendl B 2018 Anal. Chem. 90 7072
Google Scholar
[53] Kavungal D, Magalhães P, Kumar S T, Kolla R, Lashuel H A, Altug H 2023 Sci. Adv. 9 eadg9644
Google Scholar
[54] López-Lorente Á I, Mizaikoff B 2016 Anal. Bioanal. Chem. 408 2875
Google Scholar
[55] Zhang W, Chen L, Chen J, Wang L, Gui X, Ran J, Xu G, Zhao H, Zeng M, Ji J, Qian L, Zhou J, Ouyang H, Zou X 2017 Adv. Healthcare Mater. 6 1700121
Google Scholar
[56] Hoarau M, Badieyan S, Marsh E N G 2017 Org. Biomol. Chem. 15 9539
Google Scholar
[57] Talari A C S, Martinez M A G, Movasaghi Z, Rehman S, Rehman I U 2017 Appl. Spectrosc. Rev. 52 456
Google Scholar
[58] Araki K, Yagi N, Ikemoto Y, Yagi H, Choong C J, Hayakawa H, Beck G, Sumi H, Fujimura H, Moriwaki T, Nagai Y, Goto Y, Mochizuki H 2015 Sci. Rep. 5 17625
Google Scholar
[59] Nabers A, Ollesch J, Schartner J, Kötting C, Genius J, Haußmann U, Klafki H, Wiltfang J, Gerwert K 2016 J. Biophotonics 9 224
Google Scholar
[60] Nabers A, Ollesch J, Schartner J, Kötting C, Genius J, Hafermann H, Klafki H, Gerwert K, Wiltfang J 2016 Anal. Chem. 88 2755
Google Scholar
[61] Lashuel H A, Overk C R, Oueslati A, Masliah E 2013 Nat. Rev. Neurosci. 14 38
Google Scholar
[62] Conway K A, Lee S J, Rochet J C, Ding T T, Williamson R E, Lansbury P T 2000 Proc. Natl. Acad. Sci. U. S. A. 97 571
Google Scholar
[63] Hardy J A, Higgins G A 1992 Science 256 184
Google Scholar
[64] Ballatore C, Lee V M Y, Trojanowski J Q 2007 Nat. Rev. Neurosci. 8 663
Google Scholar
-
图 1 基于石墨烯/金纳米腔室的n-AGP (a) 石墨烯/金纳米腔室结构示意图; (b) 共振频率为1600 cm–1时, AGP沿x轴的电场强度分布图(Ex); (c) n-AGP, GP和AGP的色散关系. 白色虚线是GP的色散关系; 蓝色的点是从不同金纳腔宽度的归一化电场强度谱提取的n-AGP色散散点; 伪彩图通过提取石墨烯/10 nm空气间隙/金结构的归一化lmrp (rp为菲涅耳反射系数) 获得, 展示了AGP的色散关系; (d) n-AGP, GP和AGP与蛋白质振动模式耦合的归一化电场强度谱. AGP与蛋白质振动模式耦合的归一化电场强度谱线放大40倍. 灰色虚线代表基线. 黑色箭头代表在酰胺I带处由于法诺共振现象造成的谱线凹陷最深处. 凹陷深度分别为3 (n-AGP+蛋白质)、1.5 (GP+蛋白质)和0.05 (AGP+蛋白质). 蛋白质宽度为60 nm、高度为10 nm. 计算模型中设置空气间隙为10 nm
Fig. 1. n-AGP based on graphene/gold nanocavity: (a) Schematic diagram of graphene/gold nanocavity; (b) electric-field distribution of AGP at a resonance frequency of 1600 cm–1; (c) dispersion of n-AGP, GP, and AGP. The white dashed line represents the dispersion of GP; the blue dots are n-AGP dispersion scatter points extracted from normalized electric-field spectra of different nanocavity widths; the false-color background is obtained by the normalized lm rp of graphene/ 10 nm air gap/ gold structure, demonstrating the dispersion relationship of AGP (rp is the Fresnel reflection coefficient); (d) normalized electric-field spectra of n-AGP, GP and AGP coupled with proteins. The normalized electric-field spectra of AGP-protein coupling is amplified by 40 times. The gray dashed line represents the baseline. The black arrow represents the deepest spectral line depression caused by the Fano resonance phenomenon in the Amide I region. The depth of the depression is 3 (n-AGP+protein), 1.5 (GP+protein), and 0.05 (AGP+protein), respectively. The protein has a width of 60 nm and a height of 10 nm. The thickness of air gap is 10 nm in the calculation model.
图 2 金纳米腔室的不同结构参数对n-AGP共振频率的影响 (a) 腔体深度; (b) 腔体宽度; (c) 腔体下表面粗糙度(Ra), Ra为算数平均粗糙度. 在图(a)—(c)的模拟计算中, 石墨烯费米能级被设置为0.5 eV
Fig. 2. Influence of different structural parameters of gold nanocavity on n-AGP resonance frequency: (a) Cavity depth; (b) width; (c) surface roughness (Ra), Ra is average roughness. In panels (a)–(c), the Fermi level of graphene is 0.5 eV.
图 3 石墨烯费米能级对n-AGP的影响 (a) 归一化电场强度谱对石墨烯费米能级的依赖性, 模拟中设置的金纳米腔室深度为10 nm, 宽度为60 nm; (b) 不同石墨烯费米能级下计算的AGP色散关系, 空气间隙为10 nm
Fig. 3. Effect of graphene Fermi level on n-AGP: (a) Dependence of normalized electric-field spectra on graphene Fermi level, and the depth of the gold nanocavity is 10 nm and the width is 60 nm in the simulation; (b) dispersion relationship of AGP corresponding to different graphene Fermi levels, and the thickness of air gap is 10 nm.
图 4 n-AGP增强nano-IR在蛋白质检测中的应用 (a) n-AGP场增强依赖频率和腔内位置变化的三维图. 白色虚线代表当x = 0 nm时, n-AGP的归一化电场强度谱. 金纳米腔室宽度为60 nm; (b) n-AGP场增强依赖频率和腔内位置变化的三维图. 蛋白质的宽度为10 nm, 高度为10 nm. 蛋白质分子的酰胺I带对应的频率为1650 cm–1, 酰胺II带对应的频率为1532 cm–1; (c) 不同尺寸蛋白质与n-AGP耦合的归一化电场强度谱. 蛋白质高度为20 nm (蓝色)、15 nm (绿色)、10 nm (红色). 蛋白质高度为10 nm时, 无增强nano-IR结构的蛋白质的归一化电场强度谱 (浅红色, 放大50倍). 蛋白质的宽度为10 nm. 灰色虚线代表基线, 黑色箭头代表由于法诺共振现象造成的谱线凹陷深度. 蛋白质高度为20, 15, 10 nm时, 谱线凹陷深度分别为1.48, 0.74, 0.06. 在模拟中, 高度为10 nm的蛋白质在酰胺I带的谐振强度为0.007
Fig. 4. Application of n-AGP-enhanced nano-IR in protein detection: (a) Three-dimensional diagram showing the dependency of n-AGP field enhancement on frequency and position. The white dashed line represents the normalized electric-field spectra of n-AGP extracted when x = 0 nm, and the width of the gold nanocavity is 60 nm; (b) three-dimensional diagram showing the dependency of n-AGP field enhancement on frequency and position. The width of the protein is 10 nm and the height is 10 nm; The frequency corresponding to the amide I band of protein molecules is 1650 cm–1, and the frequency corresponding to the amide II band is 1532 cm–1;(c) normalized electric-field spectra of proteins with different sizes coupled with n-AGP. Normalized electric-field spectra of proteins with height of 20 nm (blue curve), 15 nm (green curve), and 10 nm (red curve) at the center of the graphene/gold nanocavity enhanced structure. When the protein height is 10 nm, there is no enhanced nano-IR structure and the normalized electric-field spectrum (light red curve) is amplified 50 times. The width of the simulated protein is 10 nm. The gray dashed line represents the baseline, and the black arrow represents the depth of spectral line depression caused by the Fano resonance phenomenon. When the protein height is 20, 15 and 10 nm, the depth of spectral line depression is 1.48, 0.74, and 0.06, respectively. The resonance intensity of the simulated protein with a height of 10 nm at amide I band is 0.007.
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[1] Yao Z, Xu S, Hu D, Chen X, Dai Q, Liu M 2020 Adv. Opt. Mater. 8 1901042
Google Scholar
[2] Chen X, Hu D, Mescall R, You G, Basov D N, Dai Q, Liu M 2019 Adv. Mater. 31 1804774
Google Scholar
[3] Lahiri B, Holland G, Centrone A 2013 Small 9 439
Google Scholar
[4] Centrone A 2015 Annu. Rev. Anal. Chem. 8 101
Google Scholar
[5] Katzenmeyer A M, Holland G, Kjoller K, Centrone A 2015 Anal. Chem. 87 3154
Google Scholar
[6] Dazzi A, Glotin F, Carminati R 2010 J. Appl. Phys. 107 124519
Google Scholar
[7] Schwartz J J, Jakob D S, Centrone A 2022 Chem. Soc. Rev. 51 5248
Google Scholar
[8] Wang L, Wang H, Xu X G 2022 Chem. Soc. Rev. 51 5268
Google Scholar
[9] Wang L, Wang H, Wagner M, Yan Y, Jakob D S, Xu X G 2017 Sci. Adv. 3 e1700255
Google Scholar
[10] Wang H, Gonzalez-Fialkowski J M, Li W, Xie Q, Yu Y, Xu X G 2021 Anal. Chem. 93 3567
Google Scholar
[11] Davies-Jones J A, Davies P R 2022 Mater. Chem. Front. 6 1552
Google Scholar
[12] Rajapaksa I, Uenal K, Wickramasinghe H K 2010 Appl. Phys. Lett. 97 073121
Google Scholar
[13] Jahng J, Fishman D A, Park S, Nowak D B, Morrison W A, Wickramasinghe H K, Potma E O 2015 Acc. Chem. Res. 48 2671
Google Scholar
[14] Xue M, Ye S, Ma X, Ye F, Wang C, Zhu L, Yang Y, Chen J 2022 J. Am. Chem. Soc. 144 20278
Google Scholar
[15] Gamage S, Howard M, Makita H, Cross B, Hastings G, Luo M, Abate Y 2018 PLoS One 13 e0199112
Google Scholar
[16] Kim S Y, Khanal D, Kalionis B, Chrzanowski W 2019 Nat. Protoc. 14 576
Google Scholar
[17] Goikoetxea M, Amenabar I, Chimenti S, Paulis M, Leiza J R, Hillenbrand R 2021 Macromolecules 54 995
Google Scholar
[18] Tri P N, Prud’homme R E 2018 Macromolecules 51 181
Google Scholar
[19] Morsch S, Liu Y, Lyon S B, Gibbon S R 2016 ACS Appl. Mater. Interfaces 8 959
Google Scholar
[20] Yang J, Hatcherian J, Hackley P C, Pomerantz A E 2017 Nat. Commun. 8 2179
Google Scholar
[21] Hassenkam T, Andersson M P, Dalby K N, Mackenzie D M A, Rosing M T 2017 Nature 548 78
Google Scholar
[22] Kebukawa Y, Kobayashi H, Urayama N, Baden N, Kondo M, Zolensky M E, Kobayashi K 2019 Proc. Natl. Acad. Sci. U. S. A. 116 753
Google Scholar
[23] Nishida J, Otomo A, Koitaya T, Shiotari A, Minato T, Iino R, Kumagai T 2024 Nano Lett. 24 836
Google Scholar
[24] Tang F, Bao P, Su Z 2016 Anal. Chem. 88 4926
Google Scholar
[25] Patabendigedara S, Nowak D, Nancarrow M J B, Clark S M 2021 Rev. Sci. Instrum. 92 023103
Google Scholar
[26] Yang X, Sun Z, Low T, Hu H, Guo X, Garcia de Abajo F J, Avouris P, Dai Q 2018 Adv. Mater. 30 e1704896
Google Scholar
[27] Hu H, Yang X, Zhai F, Hu D, Liu R, Liu K, Sun Z, Dai Q 2016 Nat. Commun. 7 12334
Google Scholar
[28] Wu C, Guo X, Duan Y, Lü W, Hu H, Hu D, Chen K, Sun Z, Gao T, Yang X, Dai Q 2022 Adv. Mater. 34 2110525
Google Scholar
[29] Rodrigo D, Limaj O, Janner D, Etezadi D, García de Abajo F J, Pruneri V, Altug H 2015 Science 349 165
Google Scholar
[30] Hu H, Yang X, Guo X, Khaliji K, Biswas S R, García de Abajo F J, Low T, Sun Z, Dai Q 2019 Nat. Commun. 10 1131
Google Scholar
[31] Bareza N, Jr, Gopalan K K, Alani R, Paulillo B, Pruneri V 2020 ACS Photonics 7 879
Google Scholar
[32] Alonso-González P, Nikitin A Y, Gao Y, Woessner A, Lundeberg M B, Principi A, Forcellini N, Yan W, Vélez S, Huber A J, Watanabe K, Taniguchi T, Casanova F, Hueso L E, Polini M, Hone J, Koppens F H L, Hillenbrand R 2017 Nat. Nanotechnol. 12 31
Google Scholar
[33] Menabde S G, Lee I H, Lee S, Ha H, Heiden J T, Yoo D, Kim T T, Low T, Lee Y H, Oh S H, Jang M S 2021 Nat. Commun. 12 938
Google Scholar
[34] Epstein I, Alcaraz D, Huang Z, Pusapati V V, Hugonin J P, Kumar A, Deputy X M, Khodkov T, Rappoport T G, Hong J Y, Peres N M R, Kong J, Smith D R, Koppens F H L 2020 Science 368 1219
Google Scholar
[35] Lundeberg M B, Gao Y, Asgari R, Tan C, Van Duppen B, Autore M, Alonso-González P, Woessner A, Watanabe K, Taniguchi T, Hillenbrand R, Hone J, Polini M, Koppens F H L 2017 Science 357 187
Google Scholar
[36] Alcaraz Iranzo D, Nanot S, Dias E J C, Epstein I, Peng C, Efetov D K, Lundeberg M B, Parret R, Osmond J, Hong J Y, Kong J, Englund D R, Peres N M R, Koppens F H L 2018 Science 360 291
Google Scholar
[37] Chen S, Autore M, Li J, Li P, Alonso-Gonzalez P, Yang Z, Martin-Moreno L, Hillenbrand R, Nikitin A Y 2017 ACS Photonics 4 3089
Google Scholar
[38] Olmon R L, Raschke M B 2012 Nanotechnology 23 444001
Google Scholar
[39] Low T, Avouris P 2014 ACS Nano 8 1086
Google Scholar
[40] Adato R, Altug H 2013 Nat. Commun. 4 2154
Google Scholar
[41] Du X, Skachko I, Barker A, Andrei E Y 2008 Nat. Nanotechnol. 3 491
Google Scholar
[42] Rickhaus P, Maurand R, Liu M H, Weiss M, Richter K, Schönenberger C 2013 Nat. Commun. 4 2342
Google Scholar
[43] Dorgan V E, Behnam A, Conley H J, Bolotin K I, Pop E 2013 Nano Lett. 13 4581
Google Scholar
[44] Hu H, Yu R, Teng H, Hu D, Chen N, Qu Y, Yang X, Chen X, McLeod A S, Alonso-González P, Guo X, Li C, Yao Z, Li Z, Chen J, Sun Z, Liu M, García de Abajo F J, Dai Q 2022 Nat. Commun. 13 1465
Google Scholar
[45] Lu Y H, Morales C, Zhao X, van Spronsen M A, Baskin A, Prendergast D, Yang P, Bechtel H A, Barnard E S, Ogletree D F, Altoe V, Soriano L, Schwartzberg A M, Salmeron M 2020 Nano Lett. 20 6364
Google Scholar
[46] Farmer D B, Rodrigo D, Low T, Avouris P 2015 Nano Lett. 15 2582
Google Scholar
[47] Zhuang B, Li S, Li S, Yin J 2021 Carbon 173 609
Google Scholar
[48] Dregely D, Neubrech F, Duan H, Vogelgesang R, Giessen H 2013 Nat. Commun. 4 2237
Google Scholar
[49] Kawata S 2001 Near-field Optics and Surface Plasmon Polaritons (Vol. 81) (Berlin: Springer Science & Business Media) p163
[50] Miao X, Luk T S, Liu P Q 2022 Adv. Mater. 34 2107950
Google Scholar
[51] Guo Q, Li C, Deng B, Yuan S, Guinea F, Xia F 2017 ACS Photonics 4 2989
Google Scholar
[52] Schwaighofer A, Montemurro M, Freitag S, Kristament C, Culzoni M J, Lendl B 2018 Anal. Chem. 90 7072
Google Scholar
[53] Kavungal D, Magalhães P, Kumar S T, Kolla R, Lashuel H A, Altug H 2023 Sci. Adv. 9 eadg9644
Google Scholar
[54] López-Lorente Á I, Mizaikoff B 2016 Anal. Bioanal. Chem. 408 2875
Google Scholar
[55] Zhang W, Chen L, Chen J, Wang L, Gui X, Ran J, Xu G, Zhao H, Zeng M, Ji J, Qian L, Zhou J, Ouyang H, Zou X 2017 Adv. Healthcare Mater. 6 1700121
Google Scholar
[56] Hoarau M, Badieyan S, Marsh E N G 2017 Org. Biomol. Chem. 15 9539
Google Scholar
[57] Talari A C S, Martinez M A G, Movasaghi Z, Rehman S, Rehman I U 2017 Appl. Spectrosc. Rev. 52 456
Google Scholar
[58] Araki K, Yagi N, Ikemoto Y, Yagi H, Choong C J, Hayakawa H, Beck G, Sumi H, Fujimura H, Moriwaki T, Nagai Y, Goto Y, Mochizuki H 2015 Sci. Rep. 5 17625
Google Scholar
[59] Nabers A, Ollesch J, Schartner J, Kötting C, Genius J, Haußmann U, Klafki H, Wiltfang J, Gerwert K 2016 J. Biophotonics 9 224
Google Scholar
[60] Nabers A, Ollesch J, Schartner J, Kötting C, Genius J, Hafermann H, Klafki H, Gerwert K, Wiltfang J 2016 Anal. Chem. 88 2755
Google Scholar
[61] Lashuel H A, Overk C R, Oueslati A, Masliah E 2013 Nat. Rev. Neurosci. 14 38
Google Scholar
[62] Conway K A, Lee S J, Rochet J C, Ding T T, Williamson R E, Lansbury P T 2000 Proc. Natl. Acad. Sci. U. S. A. 97 571
Google Scholar
[63] Hardy J A, Higgins G A 1992 Science 256 184
Google Scholar
[64] Ballatore C, Lee V M Y, Trojanowski J Q 2007 Nat. Rev. Neurosci. 8 663
Google Scholar
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